1. Field of the Invention
The present invention relates to generally to decision-feedback equalization techniques and, in particular, to adaptation techniques for sampling in decision-feedback equalization.
2. Description of the Related Art
Digital communication receivers typically sample an analog waveform and detect the sampled data. Signals arriving at a receiver might be corrupted by intersymbol interference (ISI), crosstalk, echo, and other noise. Thus, receivers typically equalize the channel, to compensate for such distortions, and decode the encoded signals at increasingly high clock rates. Decision-feedback equalization (DFE) is a widely-used technique for removing intersymbol interference and other noise. Generally, decision-feedback equalization utilizes a nonlinear equalizer to equalize the channel using a feedback loop based on previously decided symbols.
In one typical DFE implementation, a received analog signal is sampled and compared to one or more thresholds to generate the detected data. A DFE correction signal, v(t), is subtracted in a feedback fashion to produce a DFE corrected signal w(t). A continuous time analog equalizer (AEQ) might be employed to equalize communication systems such as a magnetic recording channel or high speed serial links employing backplanes, copper cables, optical links, etc. The AEQ in general might be implemented as one of a wide variety of topologies and configurations from the Butterworth, Chebyshev and Equiripple filters, or other topologies. The AEQ might also be implemented as a filter of varying order. Despite the variation in AEQ topologies, two high level parameters are typically relevant for equalization: high frequency peaking and bandwidth. Peaking allows for boosting back the high frequency components attenuated by a channel, and a finite bandwidth limits circuit noise and minimizes aliasing due to sampling which often follows the AEQ. AEQ parameters are desirably self adapting as the required peaking depends on the channel loss.
Known approaches for adapting the AEQ parameters might comprise implementing the entire LMS algorithm in the analog domain, or employing analog circuit techniques to adapt the AEQ parameters. Approaches employing a digital adaptation algorithm might typically require pre-calibration and measurement of channel pulse responses. Other approaches might employ a linear combiner form of an AEQ and require emulating analog filter states with digital equivalent states for adaptation. Thus, there is a need for adapting a class of AEQ transfer functions based on coefficients related to the impulse response of the AEQ without a priori channel training or measurement.